Inclusion of a priori information in genome-wide association analysis.

Genetic Epidemiology
Nathan L TintleHeike Bickeböller

Abstract

Genome-wide association studies (GWAS) continue to gain in popularity. To utilize the wealth of data created more effectively, a variety of methods have recently been proposed to include a priori information (e.g., biologically interpretable sets of genes, candidate gene information, or gene expression) in GWAS analysis. Six contributions to Genetic Analysis Workshop 16 Group 11 applied novel or recently proposed methods to GWAS of rheumatoid arthritis and heart disease related phenotypes. The results of these analyses were a variety of novel candidate genes and sets of genes, in addition to the validation of well-known genotype-phenotype associations. However, because many methods are relatively new, they would benefit from further methodological research to ensure that they maintain type I error rates while increasing power to find additional associations. When methods have been adapted from other study types (e.g., gene expression data analysis or linkage analysis), the lessons learned there should be used to guide implementation of techniques. Lastly, many open research questions exist concerning the logistic details of the origin of the a priori information and the way to incorporate it. Overall, our group has demonstrated...Continue Reading

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Dec 19, 2009·BMC Proceedings·Melanie SohnsHeike Bickeböller

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Citations

Jun 28, 2011·Genome Biology·Nicola SegataCurtis Huttenhower
Feb 4, 2012·Statistics in Medicine·Babak ShahbabaZhaoxia Yu
Nov 19, 2010·Nature Reviews. Genetics·Kai WangHakon Hakonarson
Sep 22, 2017·Nucleic Acids Research·Alexandre GouyLaurent Excoffier

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